بهینه‌سازی تعداد، محل و اندازه منابع تولید پراکنده و جبران ساز سنکرون استاتیکی با روش الگوریتم ژنتیک
محورهای موضوعی : مهندسی الکترونیک
محمد خادم
1
,
مصطفی اسماعیل بیگ
2
1 - دانشگاه آزاد اسلامی بوشهر، بوشهر، ایران
2 - دانشگاه آزاد اسلامی بوشهر، بوشهر، ایران
کلید واژه: الگوریتم ژنتیک, منابع تولیدات پراکنده, جبران ساز سنکرون استاتیکی, بهینه‌سازی,
چکیده مقاله :
استفاده از ادوات فکتس و منابع تولید پراکنده به‌عنوان یک تکنولوژی در سیستم‌های قدرت و توزیع هر روز افزایش می‌یابد. این تجهیزات بر روی پارامترهای متعددی همچون پروفیل ولتاژ، تلفات خط، جریان اتصال کوتاه، پایداری و قابلیت اطمینان سیستم تأثیرگذار می‌باشند و بنابراین تعیین محل بهینه نصب، تعداد و اندازه آن‌ها یکی از مسائل مهمی می‌باشد که مورد توجه می‌باشد زیرا نصب این ادوات و منابع در محل‌های غیر بهینه سبب افزایش تلفات سیستم و تأثیر منفی بر پروفیل ولتاژ و سایر پارامترهای سیستم می‌شود. در این مقاله به بهینه‌سازی هم‌زمان تعداد، محل و اندازه منابع تولیدات پراکنده و جبران ساز سنکرون استاتیکی پرداخته شده و به‌منظور حل مسئله بهینه‌سازی از الگوریتم ژنتیک (GA) استفاده شده است. به همین منظور تابع چند هدفه شامل هزینه‌های بهره‌برداری و تولید منابع تولیدات پراکنده و جبران ساز سنکرون استاتیکی و قابلیت بارپذیری سیستم ارائه شده است و نتایج حاصل از شبیه‌سازی برای دو شبکه نمونه 33 و 69 باس استاندارد IEEE مورد تحلیل و بررسی قرار گرفت. نتایج به دست آمده نشان می‌دهد که با افزایش بارپذیری سیستم، هزینه افزایش می‌یابد زیرا تعداد تجهیزات مربوط به منابع تولید پراکنده و جبران ساز سنکرون استاتیکی بیشتر می‌شود. همچنین بهینه‌سازی و جایابی هم‌زمان این تجهیزات، سبب کاهش هزینه‌ها و افزایش بارپذیری سیستم توزیع می‌شود.
Recently the use of AC transmission system (FACTS) devices and distributed generation resources as technology in power and distribution systems is increasing. This equipment affects various parameters such as voltage profile, line losses, short circuit current, stability, and reliability of the system, and therefore determining the optimal installation location, their number and size are one of the important issues that are considered because the installation of these devices and Resources in non-optimal locations increase system losses and negatively affect voltage profiles and other system parameters. In this paper, the simultaneous optimization of the number, location, and size of distributed generation resources and static synchronous compensation is used and in order to solve the optimization problem, a genetic algorithm (GA) is used. For this purpose, a multi-objective function including operating costs and generation of distributed generation resources and static synchronous compensation and system load capacity are presented and the simulation results were analyzed for two 33 and 69 IEEE standard networks. The results show that with increasing system load, the cost increases because the number of equipment related to distributed generation sources and static synchronous compensator increases. Also, the simultaneous optimization and placement of this equipment reduces costs and increases the load capacity of the distribution system.
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[2] S. Eyad, et al. "Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and DSTATCOM under Uncertainties of Loads and Solar Irradiance." IEEE Access 9 ,2021,pp. 26541-26555.
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[4] C. Abdeljebbar, et al. "Historical Literature Review of Optimal Placement of Electrical Devices in Power Systems: Critical Analysis of Renewable Distributed Generation Efforts." IEEE Systems Journal (2020).
[5] L. Yinghai, et al. "The chaos-based shuffled frog leaping algorithm and its application." in IEEE international conference on natural computation, 2008,pp.224-227.
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[7] F. Luis, J. Dent, and P. Harrison. "Distribution network capacity assessment: Variable DG and active networks." IEEE Transactions on Power Systems, vol.25, no.1,pp. 87-95, 2009.
[8] F. Luis, and P. Harrison. "Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation." IEEE Transactions on Power Systems, vol.26, no.1, pp.198-205,2010.
[9] T. Yuvaraj, and R. Kuppan. "Multi-objective simultaneous placement of DG and DSTATCOM using novel lightning search algorithm." Journal of applied research and technology, vol.15,no.5,pp. 477-491,2017.
[10] T. Khan, and S. Siddiqui. "Optimal Placement of Distributed Generation and D-STATCOM in Radial Distribution Network." Smart Science vol.6,no.2, pp. 125-133, 2018.
[11] M. Nazari, et al. "Optimal multi-objective D-STATCOM placement using MOGA for THD mitigation and cost minimization." Journal of Intelligent & Fuzzy Systems vol.35,no.2, pp. 2339-2348, 2018.
[12] W. Lingfeng, and C. Singh. "Reliability-constrained optimum placement of reclosers and distributed generators in distribution networks using an ant colony system algorithm." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.38, no.6, pp. 757-764, 2008.
[13] R. K. Singh and S. K. Goswami. "Optimal siting and sizing of distributed generations in radial and networked systems considering different voltage dependent static load models." in IEEE 2nd International Power and Energy Conference, 2008, pp.105-108.
[14] A.Safari, et al. "multi-objective model for simultaneous distribution networks reconfiguration and allocation of D-STATCOM under uncertainties of RESs." International Journal of Ambient Energy, vol.35,no.2, pp. 1-10, 2020.
[15], A. Selim, S. Kamel, and F. Jurado. "Hybrid optimization technique for optimal placement of DG and D-STATCOM in distribution networks." In Twentieth International Middle East Power Systems Conference (MEPCON). IEEE, 2018, pp.1-4.
[16] S. Devi, and M. Geethanjali. "Optimal location and sizing determination of Distributed Generation and DSTATCOM using Particle Swarm Optimization algorithm." International Journal of Electrical Power & Energy Systems vol.62, pp. 562-570, 2014.
[17] P. Prakash, and K. Khatod. "Optimal sizing and siting techniques for distributed generation in distribution systems: A review." Renewable and sustainable energy reviews vol.57, pp. 111-130, 2016.
[18] D. Sangeeta, D. Das, and A. Patra. "Operation of distribution network with optimal placement and sizing of dispatchable DGs and shunt capacitors." Renewable and Sustainable Energy Reviews, vol. 113, pp. 209-219, 2019.
[19] K. Shamte, et al. "An improved backward/forward sweep power flow method based on network tree depth for radial distribution systems." Journal of Electrical Systems and Information Technology, vol.8, no.1, pp.1-18, 2021.
[20] R. Rahul, and P. Suresh Babu, "A backward/forward method for solving load flows in droop-controlled microgrids." Control Applications in Modern Power System. Springer, Singapore,vol.33, pp. 367-377, 2021.
[21] G. Kunal Sandip, S. Jayaraj, and M. Lee, "A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models." International Journal of Energy Research, vol.45,no.1, pp. 6-35, 2021.
[22] G. Sandip, S. Jayaraj, and . Lee, "A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models." International Journal of Energy Research, vol.45,no.1, pp. 6-35, 2021.35.
[23] C. Valentina, et al. "Ga-based solutions comparison for storage strategies optimization for an automated warehouse." In IEEE Ninth International Conference on Intelligent Systems Design and Applications, 2009, pp. 113-116.
[24] M. Hosseini, and H. Shayanfar, "Regular paper modeling of series and shunt distribution FACTS devices in distribution systems load flow." J. Electrical Systems, vol.4, no.4 ,pp. 1-12, 2008.
[25] B. Boštjan, and I. Papič, "A new mathematical model and control of D-StatCom for operation under unbalanced conditions." Electric Power Systems Research, vol.72, no.3, pp. 279-287, 2004.
_||_[1] M. Ziad, et al. "A novel distributed generation planning algorithm via graphically-based network reconfiguration and soft open points placement using Archimedes optimization algorithm." Ain Shams Engineering Journal (2021).
[2] S. Eyad, et al. "Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and DSTATCOM under Uncertainties of Loads and Solar Irradiance." IEEE Access 9 ,2021,pp. 26541-26555.
[3] J. Singh, and A. Gupta. "Optimal Share of DG and DSTATCOM in Distribution Network Using Firefly Algorithm." Recent Advances in Power Systems. Springer, Singapore, 2021. pp.497-508.
[4] C. Abdeljebbar, et al. "Historical Literature Review of Optimal Placement of Electrical Devices in Power Systems: Critical Analysis of Renewable Distributed Generation Efforts." IEEE Systems Journal (2020).
[5] L. Yinghai, et al. "The chaos-based shuffled frog leaping algorithm and its application." in IEEE international conference on natural computation, 2008,pp.224-227.
[6] A. Khorsandi A. Alimardani B. Vahidi S.H. Hosseinian, “Hybrid shuffled frog leaping algorithm and Nelder–Mead simplex search for optimal reactive power dispatch,” IET Gener. Transm. Distrib, vol. 5, no. 2, pp. 249–256, 2011.
[7] F. Luis, J. Dent, and P. Harrison. "Distribution network capacity assessment: Variable DG and active networks." IEEE Transactions on Power Systems, vol.25, no.1,pp. 87-95, 2009.
[8] F. Luis, and P. Harrison. "Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation." IEEE Transactions on Power Systems, vol.26, no.1, pp.198-205,2010.
[9] T. Yuvaraj, and R. Kuppan. "Multi-objective simultaneous placement of DG and DSTATCOM using novel lightning search algorithm." Journal of applied research and technology, vol.15,no.5,pp. 477-491,2017.
[10] T. Khan, and S. Siddiqui. "Optimal Placement of Distributed Generation and D-STATCOM in Radial Distribution Network." Smart Science vol.6,no.2, pp. 125-133, 2018.
[11] M. Nazari, et al. "Optimal multi-objective D-STATCOM placement using MOGA for THD mitigation and cost minimization." Journal of Intelligent & Fuzzy Systems vol.35,no.2, pp. 2339-2348, 2018.
[12] W. Lingfeng, and C. Singh. "Reliability-constrained optimum placement of reclosers and distributed generators in distribution networks using an ant colony system algorithm." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.38, no.6, pp. 757-764, 2008.
[13] R. K. Singh and S. K. Goswami. "Optimal siting and sizing of distributed generations in radial and networked systems considering different voltage dependent static load models." in IEEE 2nd International Power and Energy Conference, 2008, pp.105-108.
[14] A.Safari, et al. "multi-objective model for simultaneous distribution networks reconfiguration and allocation of D-STATCOM under uncertainties of RESs." International Journal of Ambient Energy, vol.35,no.2, pp. 1-10, 2020.
[15], A. Selim, S. Kamel, and F. Jurado. "Hybrid optimization technique for optimal placement of DG and D-STATCOM in distribution networks." In Twentieth International Middle East Power Systems Conference (MEPCON). IEEE, 2018, pp.1-4.
[16] S. Devi, and M. Geethanjali. "Optimal location and sizing determination of Distributed Generation and DSTATCOM using Particle Swarm Optimization algorithm." International Journal of Electrical Power & Energy Systems vol.62, pp. 562-570, 2014.
[17] P. Prakash, and K. Khatod. "Optimal sizing and siting techniques for distributed generation in distribution systems: A review." Renewable and sustainable energy reviews vol.57, pp. 111-130, 2016.
[18] D. Sangeeta, D. Das, and A. Patra. "Operation of distribution network with optimal placement and sizing of dispatchable DGs and shunt capacitors." Renewable and Sustainable Energy Reviews, vol. 113, pp. 209-219, 2019.
[19] K. Shamte, et al. "An improved backward/forward sweep power flow method based on network tree depth for radial distribution systems." Journal of Electrical Systems and Information Technology, vol.8, no.1, pp.1-18, 2021.
[20] R. Rahul, and P. Suresh Babu, "A backward/forward method for solving load flows in droop-controlled microgrids." Control Applications in Modern Power System. Springer, Singapore,vol.33, pp. 367-377, 2021.
[21] G. Kunal Sandip, S. Jayaraj, and M. Lee, "A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models." International Journal of Energy Research, vol.45,no.1, pp. 6-35, 2021.
[22] G. Sandip, S. Jayaraj, and . Lee, "A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models." International Journal of Energy Research, vol.45,no.1, pp. 6-35, 2021.35.
[23] C. Valentina, et al. "Ga-based solutions comparison for storage strategies optimization for an automated warehouse." In IEEE Ninth International Conference on Intelligent Systems Design and Applications, 2009, pp. 113-116.
[24] M. Hosseini, and H. Shayanfar, "Regular paper modeling of series and shunt distribution FACTS devices in distribution systems load flow." J. Electrical Systems, vol.4, no.4 ,pp. 1-12, 2008.
[25] B. Boštjan, and I. Papič, "A new mathematical model and control of D-StatCom for operation under unbalanced conditions." Electric Power Systems Research, vol.72, no.3, pp. 279-287, 2004.